library(dplyr)
##
## Attaching package: 'dplyr'
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## filter, lag
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## intersect, setdiff, setequal, union
library(readr)
library(ggplot2)
library(plotly)
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## Attaching package: 'plotly'
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## last_plot
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library(DT)
library(readxl)
library(graphics)
library(stats)
estadisticas_policiales <-
read_excel("C:/Users/Dell/Documents/I-2022/Procesamiento de Datos Geograficos/Tarea 2/estadisticaspoliciales2021.xls")
estadisticas_policiales$Fecha <- as.Date(estadisticas_policiales$Fecha, format = "%d/%m/%Y")
estadisticas_policiales <-
estadisticas_policiales %>%
select(Delito, Fecha, "Víctima" = Victima, Edad, "Género" = Genero, Provincia, "Cantón" = Canton ) %>%
mutate(Fecha = as.Date(Fecha, format = "%d/%m/%Y"))
estadisticas_policiales %>%
datatable(options = list(
pageLength = 10,
language = list(url = '//cdn.datatables.net/plug-ins/1.10.11/i18n/Spanish.json')
))
## Warning in instance$preRenderHook(instance): It seems your data is too big
## for client-side DataTables. You may consider server-side processing: https://
## rstudio.github.io/DT/server.html
{r Gráfico de barras con la cantidad de delitos por tipo de
estadisticas_policiales <-
estadisticas_policiales %>%
mutate(mes = format(Fecha, "%B")) %>%
select(Delito, Fecha, Víctima, Edad, Género, Provincia, Cantón, mes) %>%
mutate(Fecha = as.Date(Fecha, format = "%d/%m/%Y"))
ggplot2_cantidad_delitos_mes <-
estadisticas_policiales %>%
ggplot(aes(x = mes)) +
geom_bar() +
ggtitle("Cantidad de delitos por mes") +
xlab("Mes") +
ylab("Cantidad de Delitos") +
theme_get()